class_docstr: Container mirroring Sklearn anomaly detection API.
docstr: Utility functions used to emulate the behavior of the Sklearn API.
  On anomaly detection (e.g. isolation forest) returns the decision function scores.
docstr_tok: ["Utility","functions","used","to","emulate","the","behavior","of","the","Sklearn","API",".","On","anomaly","detection","(","e.g",".","isolation","forest",")","returns","the","decision","function","scores","."]
code: def decision_function(self, *inputs):
      """
      Utility functions used to emulate the behavior of the Sklearn API.
      On anomaly detection (e.g. isolation forest) returns the decision function scores.
      """
      scores = self._run(self._decision_function, *inputs)
  
      # Backward compatibility for sklearn <= 0.21
      if constants.IFOREST_THRESHOLD in self._extra_config:
          scores += self._extra_config[constants.IFOREST_THRESHOLD]
      return scores
  
imports: from abc import ABC, abstractmethod
  from hummingbird.ml._utils import pandas_installed
  from hummingbird.ml.operator_converters import constants
label_desc: Use image node_id 3 for calling the SklearnContainerAnomalyDetection obj's underlying member method code with example usage: obj.decision_function() and returns: scores